This paper deals with the active vibration control of a quarter-vehicle suspension system. Damping control methods investigated in this paper are: higher-order sliding mode control (HOSMC) based on super twisting algorithm (STA), first-order sliding mode control (FOSMC), integral sliding mode control (ISMC), proportional integral derivative (PID), linear quadratic regulator (LQR) and passive suspension system. Performance comparison of different active controllers are analyzed in terms of vertical displacement, suspension travel and wheel deflection. The theoretical, quantitative and qualitative analysis verify that the STA-based HOSMC exhibits better performance as well as negate the undesired disturbances with respect to FOSMC, ISMC, PID, LQR and passive suspension system. Furthermore, it is also robust to intrinsic bounded uncertain dynamics of the model.

As a miniaturized direct injection (DI) solution, the pump injector is of great significance for small aviation piston engines, such as two-stroke heavy-fuel engines. The accurate control of the timing and amount of injections is an important application prerequisite. In this paper, the flow characteristics of a pump injector with different driving circuits and parameters are investigated. The effects of the power supply and freewheeling circuits on the opening and closing delays are theoretically studied. A schematic of a self-designed logic control circuit is proposed, and it achieves peak and hold current control without software intervention. Simulations and experiments are carried out, and the drive current and flow characteristics in different states are measured. The results show that when driving a solenoid with a high internal resistance of 2.70 Ω, a single power supply is more suitable, and the variable-freewheeling circuit exhibits excellent performance in terms of control accuracy and energy recovery. Notably, the invariable-freewheeling circuit results in nonlinear flow characteristics, and the nonlinear segment corresponds to the drive current transitioning from the peak value to the hold value. In contrast, the variable-freewheeling circuit overcomes this problem. To comprehensively consider solenoid opening and closing delays, energy consumption and the linearity of the flow characteristics during injection, a peak current of 10 A and a hold current of 8 A are adopted.

Multi-vehicle tracking is used in advanced driver assistance systems to track obstacles, which is fundamental for high-level tasks. It requires real-time performance while dealing with object illumination variations and deformations. To this end, we propose a novel multi-vehicle tracking algorithm based on a monocular camera in driver view. It follows the tracking-by-detection paradigm and integrates detection and appearance descriptors into a single network. The one-stage detection approach consists of a backbone, a modified BiFPN as a neck layer, and three prediction heads. The data association consists of a two-step matching strategy together with a Kalman filter. Experimental results demonstrate that the proposed approach outperforms state-of-the-art algorithms. It is also able to solve the tracking problem in driving scenarios while maintaining 16 FPS on the test dataset.

The performance of the centrifugal compressor, which is the main component of the electric supercharger, significantly impacts the engine’s dynamics, economy, emissions, and responsiveness. The purpose of this paper is to enhance the aerodynamic performance of the centrifugal compressor of the electric supercharger for the two-stroke engine by optimizing the design of its impeller and diffuser parameters. The paper employs the numerical simulation method and applies the Spalart–Allmaras turbulence model to solve the RANS equations to analyze the impact of impeller-related parameters on the centrifugal compressor’s performance. Subsequently, the paper optimizes the initial model parameters based on the simulation results and confirms its performance through an experiment. The findings indicate that enhancing the isentropic efficiency and pressure ratio of the compressor can be achieved by increasing the number of blades on the impeller, selecting an appropriate blade backward angle, and increasing the relative outlet width. After optimization, the compressor’s efficiency can achieve 0.842, the pressure ratio can reach 1.49 with a working margin of 22%, and the efficiency is enhanced by 1.4%, while the pressure ratio is increased by 1.8% compared to the pre-optimization state. Moreover, the optimized model is experimentally validated to meet the design requirements.

A novel intelligent extraction method based on time–frequency analysis and deep learning semantic segmentation were proposed for detailing electromagnetic pulse (EMP) sensitive frequency bands of engine digital controllers. In this method, wavelet transformation was performed to convert EMP-coupled signals into image data sets. Two semantic segmentation networks, namely fully convolutional networks and DeepLabV3+, were used to train and verify the obtained image data sets. The performance of the two models was compared using the confusion matrix and Kappa coefficient. Threshold segmentation, binarization, and average pooling operations were sequentially performed on the feature map output of the networks, and EMP characteristic pixel percentage was calculated using the time–frequency map. The original image was segmented by using the result of the binarization of the feature map output by the last layer of the network as a mask. This technique is immune to the interference caused by noise pixels when calculating the spectrum index. The proposed method has improved the EMP feature extraction accuracy of the model, and the overall performance of the DeepLabV3+ network model is proper. It was performed to obtain a theoretical basis for subsequent targeted EMP protection.